Large Language Model-Generated Messages to Improve Guideline-Directed Medical Therapy in Heart Failure
Brigham and Women's Hospital
Summary
This study is an investigator-initiated, cluster-randomized implementation trial evaluating a large language model (LLM)-based clinical decision support (CDS) tool designed to improve guideline-directed medical therapy (GDMT) for adult patients with heart failure seen in outpatient cardiology clinics at Mass General Brigham. For eligible heart failure encounters, the CDS tool reviews existing electronic health record (EHR) data, including diagnoses, medications, vital signs, laboratory results, and recent notes, and generates brief, clinician-facing messages suggesting opportunities to initiate or optimize GDMT and highlighting relevant safety considerations. Messages are delivered to cardiology providers via Epic InBasket and/or institutional email prior to scheduled visits. The tool is advisory only and cannot place orders or change medications automatically; all treatment decisions remain at the discretion of the treating clinician and patient. Cardiology providers are assigned at the provider/clinic level to early implementation of the CDS tool versus usual care (no messages) during the initial phase. The primary outcome is GDMT optimization within 30 days of an index visit. Secondary outcomes include feasibility of CDS generation and delivery and a 30-day safety composite (e.g., heart failure hospitalization, acute kidney injury, hyperkalemia, hypotension or bradyarrhythmia plausibly related to GDMT).
Description
Overview and Rationale Guideline-directed medical therapy (GDMT) for heart failure reduces hospitalizations and mortality, yet substantial underuse and suboptimal titration persist in routine practice, even in specialty cardiology clinics. Barriers include limited visit time, complex comorbidities, fragmented information across notes and structured data, and uncertainty about contraindications or prior intolerance. Electronic clinical decision support (CDS) tools that synthesize key patient information and highlight GDMT opportunities at the point of care may help close these gaps. Large lang…
Eligibility
- Age range
- 18–85 years
- Sex
- All
- Healthy volunteers
- No
Inclusion Criteria: * Age ≥18 years * Scheduled outpatient visit with a participating cardiology provider in an MGB outpatient cardiology clinic * At least one prior cardiology clinic visit in the MGB system within the past 2 years * Diagnosis of heart failure by ICD code within the past 2 years * Heart failure diagnosis supported by at least one of the following: * Current or recent use of a loop diuretic * Left ventricular ejection fraction ≤40% on the most recent echocardiogram * Explicit documentation of heart failure diagnosis or heart failure signs/symptoms in a prior cardiology note E…
Interventions
- DeviceLLM-GDMT Clinical Decision Support Tool
Software-only, large language model-based clinical decision support tool that reviews structured and unstructured EHR data for adult heart failure patients and generates brief, clinician-facing messages suggesting opportunities to initiate or optimize guideline-directed medical therapy (GDMT) and highlighting relevant safety considerations. Messages are delivered to cardiology providers via Epic InBasket and/or institutional email prior to eligible outpatient visits. The tool is advisory only and cannot place orders or directly change medications; all treatment decisions remain at the discretion of the treating clinician and patient.
Location
- Mass General BrighamBoston, Massachusetts